



import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


a      :  Dict			     =			logging.get_logger(__name__)







class 				_a				(   _lowerCAmelCase					):
			A      		=	['''input_features''', '''is_longer''']

			def __init__(self,   SCREAMING_SNAKE_CASE_=64,   SCREAMING_SNAKE_CASE_=48000,   SCREAMING_SNAKE_CASE_=480,   SCREAMING_SNAKE_CASE_=10,   SCREAMING_SNAKE_CASE_=1024,   SCREAMING_SNAKE_CASE_=0.0,   SCREAMING_SNAKE_CASE_=False,   SCREAMING_SNAKE_CASE_ = 0,   SCREAMING_SNAKE_CASE_ = 14000,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = "fusion",   SCREAMING_SNAKE_CASE_ = "repeatpad",   **SCREAMING_SNAKE_CASE_,   )       ->   Union[str, Any]:
						super().__init__(
						    feature_size=SCREAMING_SNAKE_CASE_,   sampling_rate=SCREAMING_SNAKE_CASE_,   padding_value=SCREAMING_SNAKE_CASE_,   return_attention_mask=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_,   )
						UpperCAmelCase_:		Dict							      =     top_db
						UpperCAmelCase_:		List[str]							      =     truncation
						UpperCAmelCase_:		Optional[int]							      =     padding
						UpperCAmelCase_:		Tuple							      =     fft_window_size
						UpperCAmelCase_:		str							      =     (fft_window_size >> 1) + 1
						UpperCAmelCase_:		Tuple							      =     hop_length
						UpperCAmelCase_:		Union[str, Any]							      =     max_length_s
						UpperCAmelCase_:		List[str]							      =     max_length_s * sampling_rate
						UpperCAmelCase_:		Any							      =     sampling_rate
						UpperCAmelCase_:		Dict							      =     frequency_min
						UpperCAmelCase_:		Tuple							      =     frequency_max
						UpperCAmelCase_:		List[Any]							      =     mel_filter_bank(
						    num_frequency_bins=self.nb_frequency_bins,   num_mel_filters=SCREAMING_SNAKE_CASE_,   min_frequency=SCREAMING_SNAKE_CASE_,   max_frequency=SCREAMING_SNAKE_CASE_,   sampling_rate=SCREAMING_SNAKE_CASE_,   norm=SCREAMING_SNAKE_CASE_,   mel_scale="""htk""",   )
						UpperCAmelCase_:		List[Any]							      =     mel_filter_bank(
						    num_frequency_bins=self.nb_frequency_bins,   num_mel_filters=SCREAMING_SNAKE_CASE_,   min_frequency=SCREAMING_SNAKE_CASE_,   max_frequency=SCREAMING_SNAKE_CASE_,   sampling_rate=SCREAMING_SNAKE_CASE_,   norm="""slaney""",   mel_scale="""slaney""",   )

			def      __snake_case       (self					)       ->   Dict[str, Any]:
						UpperCAmelCase_:		List[Any]							      =     copy.deepcopy(self.__dict__					)
						UpperCAmelCase_:		Tuple							      =     self.__class__.__name__
						if "mel_filters" in output:
									del output["mel_filters"]
						if "mel_filters_slaney" in output:
									del output["mel_filters_slaney"]
						return output

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None					)       ->   np.ndarray:
						UpperCAmelCase_:		List[Any]							      =     spectrogram(
						    SCREAMING_SNAKE_CASE_,   window_function(self.fft_window_size,   """hann"""					),   frame_length=self.fft_window_size,   hop_length=self.hop_length,   power=2.0,   mel_filters=SCREAMING_SNAKE_CASE_,   log_mel="""dB""",   )
						return log_mel_spectrogram.T

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   Dict:
						UpperCAmelCase_:		Optional[int]							      =     np.array_split(list(range(0,   total_frames - chunk_frames + 1					)					),   3					)
						if len(ranges[1]					) == 0:
									# if the audio is too short, we just use the first chunk
									UpperCAmelCase_:		Optional[int]							      =     [0]
						if len(ranges[2]					) == 0:
									# if the audio is too short, we just use the first chunk
									UpperCAmelCase_:		List[str]							      =     [0]
						# randomly choose index for each part
						UpperCAmelCase_:		List[Any]							      =     np.random.choice(ranges[0]					)
						UpperCAmelCase_:		Optional[int]							      =     np.random.choice(ranges[1]					)
						UpperCAmelCase_:		Any							      =     np.random.choice(ranges[2]					)

						UpperCAmelCase_:		List[Any]							      =     mel[idx_front : idx_front + chunk_frames, :]
						UpperCAmelCase_:		Any							      =     mel[idx_middle : idx_middle + chunk_frames, :]
						UpperCAmelCase_:		int							      =     mel[idx_back : idx_back + chunk_frames, :]

						UpperCAmelCase_:		List[str]							      =     torch.tensor(mel[None, None, :]					)
						UpperCAmelCase_:		Optional[int]							      =     torch.nn.functional.interpolate(
						    SCREAMING_SNAKE_CASE_,   size=[chunk_frames, 64],   mode="""bilinear""",   align_corners=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		int							      =     mel_shrink[0][0].numpy()
						UpperCAmelCase_:		List[Any]							      =     np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back],   axis=0					)
						return mel_fusion

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   np.array:
						if waveform.shape[0] > max_length:
									if truncation == "rand_trunc":
												UpperCAmelCase_:		Dict							      =     True
												# random crop to max_length (for compatibility) -> this should be handled by self.pad
												UpperCAmelCase_:		List[Any]							      =     len(SCREAMING_SNAKE_CASE_					) - max_length
												UpperCAmelCase_:		Any							      =     np.random.randint(0,   overflow + 1					)
												UpperCAmelCase_:		List[Any]							      =     waveform[idx : idx + max_length]
												UpperCAmelCase_:		Union[str, Any]							      =     self._np_extract_fbank_features(SCREAMING_SNAKE_CASE_,   self.mel_filters_slaney					)[None, :]
									elif truncation == "fusion":
												UpperCAmelCase_:		int							      =     self._np_extract_fbank_features(SCREAMING_SNAKE_CASE_,   self.mel_filters					)
												UpperCAmelCase_:		List[str]							      =     max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
												UpperCAmelCase_:		Union[str, Any]							      =     mel.shape[0]
												if chunk_frames == total_frames:
															# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
															# In this case, we just use the whole audio.
															UpperCAmelCase_:		Union[str, Any]							      =     np.stack([mel, mel, mel, mel],   axis=0					)
															UpperCAmelCase_:		List[str]							      =     False
												else:
															UpperCAmelCase_:		Optional[Any]							      =     self._random_mel_fusion(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)
															UpperCAmelCase_:		Union[str, Any]							      =     True
									else:
												raise NotImplementedError(f'data_truncating {truncation} not implemented'					)

						else:
									UpperCAmelCase_:		str							      =     False
									# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
									if waveform.shape[0] < max_length:
												if padding == "repeat":
															UpperCAmelCase_:		List[Any]							      =     int(max_length / len(SCREAMING_SNAKE_CASE_					)					)
															UpperCAmelCase_:		List[Any]							      =     np.stack(np.tile(SCREAMING_SNAKE_CASE_,   n_repeat + 1					)					)[:max_length]
												if padding == "repeatpad":
															UpperCAmelCase_:		Union[str, Any]							      =     int(max_length / len(SCREAMING_SNAKE_CASE_					)					)
															UpperCAmelCase_:		List[Any]							      =     np.stack(np.tile(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)					)
												UpperCAmelCase_:		List[Any]							      =     np.pad(SCREAMING_SNAKE_CASE_,   (0, max_length - waveform.shape[0]),   mode="""constant""",   constant_values=0					)

									if truncation == "fusion":
												UpperCAmelCase_:		Optional[Any]							      =     self._np_extract_fbank_features(SCREAMING_SNAKE_CASE_,   self.mel_filters					)
												UpperCAmelCase_:		Dict							      =     np.stack([input_mel, input_mel, input_mel, input_mel],   axis=0					)
									else:
												UpperCAmelCase_:		List[Any]							      =     self._np_extract_fbank_features(SCREAMING_SNAKE_CASE_,   self.mel_filters_slaney					)[None, :]

						return input_mel, longer

			def __call__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   BatchFeature:
						UpperCAmelCase_:		Tuple							      =     truncation if truncation is not None else self.truncation
						UpperCAmelCase_:		List[Any]							      =     padding if padding else self.padding

						if sampling_rate is not None:
									if sampling_rate != self.sampling_rate:
												raise ValueError(
												    f'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
												    f' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
												    f' was sampled with {self.sampling_rate} and not {sampling_rate}.'					)
						else:
									logger.warning(
									    """It is strongly recommended to pass the `sampling_rate` argument to this function. """
									    """Failing to do so can result in silent errors that might be hard to debug."""					)

						UpperCAmelCase_:		List[str]							      =     isinstance(SCREAMING_SNAKE_CASE_,   np.ndarray					) and len(raw_speech.shape					) > 1
						if is_batched_numpy and len(raw_speech.shape					) > 2:
									raise ValueError(f'Only mono-channel audio is supported for input to {self}'					)
						UpperCAmelCase_:		Dict							      =     is_batched_numpy or (
						    isinstance(SCREAMING_SNAKE_CASE_,   (list, tuple)					) and (isinstance(raw_speech[0],   (np.ndarray, tuple, list)					))
						)

						if is_batched:
									UpperCAmelCase_:		List[Any]							      =     [np.asarray(SCREAMING_SNAKE_CASE_,   dtype=np.floataa					) for speech in raw_speech]
						elif not is_batched and not isinstance(SCREAMING_SNAKE_CASE_,   np.ndarray					):
									UpperCAmelCase_:		int							      =     np.asarray(SCREAMING_SNAKE_CASE_,   dtype=np.floataa					)
						elif isinstance(SCREAMING_SNAKE_CASE_,   np.ndarray					) and raw_speech.dtype is np.dtype(np.floataa					):
									UpperCAmelCase_:		Dict							      =     raw_speech.astype(np.floataa					)

						# always return batch
						if not is_batched:
									UpperCAmelCase_:		Dict							      =     [np.asarray(SCREAMING_SNAKE_CASE_					)]

						# convert to mel spectrogram, truncate and pad if needed.
						UpperCAmelCase_:		List[str]							      =     [
						    self._get_input_mel(SCREAMING_SNAKE_CASE_,   max_length if max_length else self.nb_max_samples,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)
						    for waveform in raw_speech
						]

						UpperCAmelCase_:		List[Any]							      =     []
						UpperCAmelCase_:		int							      =     []
						for mel, longer in padded_inputs:
									input_mel.append(SCREAMING_SNAKE_CASE_					)
									is_longer.append(SCREAMING_SNAKE_CASE_					)

						if truncation == "fusion" and sum(SCREAMING_SNAKE_CASE_					) == 0:
									# if no audio is longer than 10s, then randomly select one audio to be longer
									UpperCAmelCase_:		List[Any]							      =     np.random.randint(0,   len(SCREAMING_SNAKE_CASE_					)					)
									UpperCAmelCase_:		Any							      =     True

						if isinstance(input_mel[0],   SCREAMING_SNAKE_CASE_					):
									UpperCAmelCase_:		Dict							      =     [np.asarray(SCREAMING_SNAKE_CASE_,   dtype=np.floataa					) for feature in input_mel]

						# is_longer is a list of bool
						UpperCAmelCase_:		Any							      =     [[longer] for longer in is_longer]

						UpperCAmelCase_:		int							      =     {"""input_features""": input_mel, """is_longer""": is_longer}
						UpperCAmelCase_:		List[str]							      =     BatchFeature(SCREAMING_SNAKE_CASE_					)

						if return_tensors is not None:
									UpperCAmelCase_:		List[Any]							      =     input_features.convert_to_tensors(SCREAMING_SNAKE_CASE_					)

						return input_features











#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist







def  lowerCAmelCase_	(*lowerCAmelCase__:		Dict  ):





			"""simple docstring"""


			with open(lowerCAmelCase__		,						"""r"""  ) as fh:
						fcntl.flock(lowerCAmelCase__		,						fcntl.LOCK_EX  )
						try:
									print(*lowerCAmelCase__  )
						finally:
									fcntl.flock(lowerCAmelCase__		,						fcntl.LOCK_UN  )


a      :  Tuple			     =			int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
a      :  Union[str, Any]			     =			torch.device('cuda', local_rank)
a      :  Any			     =			socket.gethostname()

a      :  Optional[Any]			     =			F'''[{hostname}-{local_rank}]'''

try:
	# test distributed
	dist.init_process_group('nccl')
	dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
	dist.barrier()

	# test cuda is available and can allocate memory
	torch.cuda.is_available()
	torch.ones(1).cuda(local_rank)

	# global rank
	a      :  Optional[Any]			     =			dist.get_rank()
	a      :  List[Any]			     =			dist.get_world_size()

	printflock(F'''{gpu} is OK (global rank: {rank}/{world_size})''')

	dist.barrier()
	if rank == 0:
		printflock(F'''pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}''')

except Exception:
	printflock(F'''{gpu} is broken''')
	raise






